Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations294
Missing cells21
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory57.6 KiB
Average record size in memory200.4 B

Variable types

Numeric18
Categorical6
Text1

Alerts

Flower_size is highly overall correlated with Flower_size_norm_std and 5 other fieldsHigh correlation
Flower_size_norm_std is highly overall correlated with Flower_size and 5 other fieldsHigh correlation
S.No. is highly overall correlated with Flower_size and 6 other fieldsHigh correlation
area is highly overall correlated with populationHigh correlation
bp_area is highly overall correlated with bp_area_norm_stdHigh correlation
bp_area_norm_std is highly overall correlated with bp_areaHigh correlation
date is highly overall correlated with S.No. and 4 other fieldsHigh correlation
day is highly overall correlated with date and 1 other fieldsHigh correlation
df_index is highly overall correlated with Flower_size and 6 other fieldsHigh correlation
entr_height is highly overall correlated with tunnel_volume_cm and 1 other fieldsHigh correlation
entr_length is highly overall correlated with tunnel_volume_cm and 1 other fieldsHigh correlation
fruit is highly overall correlated with fruit_norm_std and 2 other fieldsHigh correlation
fruit_norm_std is highly overall correlated with fruit and 2 other fieldsHigh correlation
length_mm is highly overall correlated with Flower_size and 5 other fieldsHigh correlation
population is highly overall correlated with Flower_size and 7 other fieldsHigh correlation
seed is highly overall correlated with fruit and 2 other fieldsHigh correlation
seed_norm_std is highly overall correlated with fruit and 2 other fieldsHigh correlation
treatment is highly overall correlated with dateHigh correlation
tunnel_length is highly overall correlated with tunnel_volume_cm and 1 other fieldsHigh correlation
tunnel_volume_cm is highly overall correlated with entr_height and 3 other fieldsHigh correlation
tunnel_volume_cm_norm_std is highly overall correlated with entr_height and 3 other fieldsHigh correlation
width_mm is highly overall correlated with Flower_size and 4 other fieldsHigh correlation
entr_length has 4 (1.4%) missing values Missing
entr_height has 5 (1.7%) missing values Missing
tunnel_volume_cm has 5 (1.7%) missing values Missing
tunnel_volume_cm_norm_std has 5 (1.7%) missing values Missing
df_index is uniformly distributed Uniform
S.No. is uniformly distributed Uniform
df_index has unique values Unique
S.No. has unique values Unique
seed has 150 (51.0%) zeros Zeros

Reproduction

Analysis started2025-05-17 08:27:20.714221
Analysis finished2025-05-17 08:28:36.301421
Duration1 minute and 15.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct294
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.14626
Minimum0
Maximum301
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:36.491618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.65
Q174.25
median151.5
Q3224.75
95-th percentile283.35
Maximum301
Range301
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation86.778772
Coefficient of variation (CV)0.5779616
Kurtosis-1.1978398
Mean150.14626
Median Absolute Deviation (MAD)75.5
Skewness-0.024871439
Sum44143
Variance7530.5553
MonotonicityStrictly increasing
2025-05-17T11:28:36.802589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301 1
 
0.3%
0 1
 
0.3%
1 1
 
0.3%
2 1
 
0.3%
3 1
 
0.3%
4 1
 
0.3%
282 1
 
0.3%
281 1
 
0.3%
280 1
 
0.3%
279 1
 
0.3%
Other values (284) 284
96.6%
ValueCountFrequency (%)
0 1
0.3%
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
ValueCountFrequency (%)
301 1
0.3%
300 1
0.3%
299 1
0.3%
298 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%
291 1
0.3%
290 1
0.3%
289 1
0.3%

S.No.
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct294
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.14626
Minimum1
Maximum302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:37.135432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.65
Q175.25
median152.5
Q3225.75
95-th percentile284.35
Maximum302
Range301
Interquartile range (IQR)150.5

Descriptive statistics

Standard deviation86.778772
Coefficient of variation (CV)0.57413775
Kurtosis-1.1978398
Mean151.14626
Median Absolute Deviation (MAD)75.5
Skewness-0.024871439
Sum44437
Variance7530.5553
MonotonicityStrictly increasing
2025-05-17T11:28:37.487504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
302 1
 
0.3%
1 1
 
0.3%
2 1
 
0.3%
3 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
283 1
 
0.3%
282 1
 
0.3%
281 1
 
0.3%
280 1
 
0.3%
Other values (284) 284
96.6%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
302 1
0.3%
301 1
0.3%
300 1
0.3%
299 1
0.3%
295 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%
291 1
0.3%
290 1
0.3%

date
Categorical

High correlation 

Distinct13
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-02-22
63 
2024-02-27
42 
2024-02-29
34 
2024-02-15
32 
2024-02-25
30 
Other values (8)
93 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters2940
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row2024-02-07
2nd row2024-02-07
3rd row2024-02-07
4th row2024-02-07
5th row2024-02-07

Common Values

ValueCountFrequency (%)
2024-02-22 63
21.4%
2024-02-27 42
14.3%
2024-02-29 34
11.6%
2024-02-15 32
10.9%
2024-02-25 30
10.2%
2024-03-07 18
 
6.1%
2024-02-11 17
 
5.8%
2024-03-06 16
 
5.4%
2024-02-21 16
 
5.4%
2024-02-08 12
 
4.1%
Other values (3) 14
 
4.8%

Length

2025-05-17T11:28:37.821415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-02-22 63
21.4%
2024-02-27 42
14.3%
2024-02-29 34
11.6%
2024-02-15 32
10.9%
2024-02-25 30
10.2%
2024-03-07 18
 
6.1%
2024-02-11 17
 
5.8%
2024-03-06 16
 
5.4%
2024-02-21 16
 
5.4%
2024-02-08 12
 
4.1%
Other values (3) 14
 
4.8%

Most occurring characters

ValueCountFrequency (%)
2 1091
37.1%
0 643
21.9%
- 588
20.0%
4 295
 
10.0%
1 87
 
3.0%
7 69
 
2.3%
5 62
 
2.1%
3 43
 
1.5%
9 34
 
1.2%
6 16
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1091
37.1%
0 643
21.9%
- 588
20.0%
4 295
 
10.0%
1 87
 
3.0%
7 69
 
2.3%
5 62
 
2.1%
3 43
 
1.5%
9 34
 
1.2%
6 16
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1091
37.1%
0 643
21.9%
- 588
20.0%
4 295
 
10.0%
1 87
 
3.0%
7 69
 
2.3%
5 62
 
2.1%
3 43
 
1.5%
9 34
 
1.2%
6 16
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1091
37.1%
0 643
21.9%
- 588
20.0%
4 295
 
10.0%
1 87
 
3.0%
7 69
 
2.3%
5 62
 
2.1%
3 43
 
1.5%
9 34
 
1.2%
6 16
 
0.5%

day
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.255102
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:39.364041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q115
median16
Q321
95-th percentile30
Maximum37
Range36
Interquartile range (IQR)6

Descriptive statistics

Standard deviation8.0131861
Coefficient of variation (CV)0.46439517
Kurtosis-0.25829385
Mean17.255102
Median Absolute Deviation (MAD)5
Skewness-0.16573617
Sum5073
Variance64.211151
MonotonicityNot monotonic
2025-05-17T11:28:39.584500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
16 63
21.4%
21 42
14.3%
23 34
11.6%
9 32
10.9%
19 30
10.2%
30 18
 
6.1%
5 17
 
5.8%
29 16
 
5.4%
15 16
 
5.4%
2 12
 
4.1%
Other values (3) 14
 
4.8%
ValueCountFrequency (%)
1 9
 
3.1%
2 12
 
4.1%
5 17
 
5.8%
9 32
10.9%
15 16
 
5.4%
16 63
21.4%
19 30
10.2%
21 42
14.3%
23 34
11.6%
29 16
 
5.4%
ValueCountFrequency (%)
37 1
 
0.3%
36 4
 
1.4%
30 18
 
6.1%
29 16
 
5.4%
23 34
11.6%
21 42
14.3%
19 30
10.2%
16 63
21.4%
15 16
 
5.4%
9 32
10.9%

population
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
KUR
171 
NET
123 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters882
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNET
2nd rowNET
3rd rowNET
4th rowNET
5th rowNET

Common Values

ValueCountFrequency (%)
KUR 171
58.2%
NET 123
41.8%

Length

2025-05-17T11:28:39.846049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:28:40.010070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
kur 171
58.2%
net 123
41.8%

Most occurring characters

ValueCountFrequency (%)
K 171
19.4%
U 171
19.4%
R 171
19.4%
N 123
13.9%
E 123
13.9%
T 123
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K 171
19.4%
U 171
19.4%
R 171
19.4%
N 123
13.9%
E 123
13.9%
T 123
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K 171
19.4%
U 171
19.4%
R 171
19.4%
N 123
13.9%
E 123
13.9%
T 123
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K 171
19.4%
U 171
19.4%
R 171
19.4%
N 123
13.9%
E 123
13.9%
T 123
13.9%

area
Categorical

High correlation 

Distinct10
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
GREEN
68 
BLUE
66 
Yellow
64 
Red
27 
PINK
21 
Other values (5)
48 

Length

Max length6
Median length5
Mean length4.6496599
Min length3

Characters and Unicode

Total characters1367
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBLUE
2nd rowBROWN
3rd rowRED
4th rowRED
5th rowGREEN

Common Values

ValueCountFrequency (%)
GREEN 68
23.1%
BLUE 66
22.4%
Yellow 64
21.8%
Red 27
 
9.2%
PINK 21
 
7.1%
RED 14
 
4.8%
BROWN 11
 
3.7%
Brown 9
 
3.1%
ORANGE 8
 
2.7%
Blue 6
 
2.0%

Length

2025-05-17T11:28:40.241611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:28:40.502105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
blue 72
24.5%
green 68
23.1%
yellow 64
21.8%
red 41
13.9%
pink 21
 
7.1%
brown 20
 
6.8%
orange 8
 
2.7%

Most occurring characters

ValueCountFrequency (%)
E 224
16.4%
l 134
9.8%
R 128
9.4%
N 108
 
7.9%
e 97
 
7.1%
B 92
 
6.7%
G 76
 
5.6%
o 73
 
5.3%
w 73
 
5.3%
U 66
 
4.8%
Other values (13) 296
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1367
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 224
16.4%
l 134
9.8%
R 128
9.4%
N 108
 
7.9%
e 97
 
7.1%
B 92
 
6.7%
G 76
 
5.6%
o 73
 
5.3%
w 73
 
5.3%
U 66
 
4.8%
Other values (13) 296
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1367
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 224
16.4%
l 134
9.8%
R 128
9.4%
N 108
 
7.9%
e 97
 
7.1%
B 92
 
6.7%
G 76
 
5.6%
o 73
 
5.3%
w 73
 
5.3%
U 66
 
4.8%
Other values (13) 296
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1367
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 224
16.4%
l 134
9.8%
R 128
9.4%
N 108
 
7.9%
e 97
 
7.1%
B 92
 
6.7%
G 76
 
5.6%
o 73
 
5.3%
w 73
 
5.3%
U 66
 
4.8%
Other values (13) 296
21.7%
Distinct93
Distinct (%)31.6%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:41.093402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0034014
Min length4

Characters and Unicode

Total characters1177
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)11.6%

Sample

1st rowN119
2nd rowN185
3rd rowN186
4th rowN186
5th rowN187
ValueCountFrequency (%)
k210 13
 
4.4%
k188 13
 
4.4%
k217 12
 
4.1%
k168 10
 
3.4%
n191 9
 
3.1%
k179 9
 
3.1%
n193 8
 
2.7%
k202 7
 
2.4%
n188 7
 
2.4%
n192 7
 
2.4%
Other values (83) 199
67.7%
2025-05-17T11:28:41.841593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 280
23.8%
K 171
14.5%
N 123
10.5%
2 114
9.7%
9 111
 
9.4%
8 94
 
8.0%
0 91
 
7.7%
7 62
 
5.3%
5 39
 
3.3%
6 33
 
2.8%
Other values (3) 59
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 280
23.8%
K 171
14.5%
N 123
10.5%
2 114
9.7%
9 111
 
9.4%
8 94
 
8.0%
0 91
 
7.7%
7 62
 
5.3%
5 39
 
3.3%
6 33
 
2.8%
Other values (3) 59
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 280
23.8%
K 171
14.5%
N 123
10.5%
2 114
9.7%
9 111
 
9.4%
8 94
 
8.0%
0 91
 
7.7%
7 62
 
5.3%
5 39
 
3.3%
6 33
 
2.8%
Other values (3) 59
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 280
23.8%
K 171
14.5%
N 123
10.5%
2 114
9.7%
9 111
 
9.4%
8 94
 
8.0%
0 91
 
7.7%
7 62
 
5.3%
5 39
 
3.3%
6 33
 
2.8%
Other values (3) 59
 
5.0%

Flower_id
Real number (ℝ)

Distinct260
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.41821
Minimum6.1
Maximum910.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:42.122524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile50.165
Q1169.35
median191.15
Q3202.275
95-th percentile218.775
Maximum910.1
Range904
Interquartile range (IQR)32.925

Descriptive statistics

Standard deviation98.996005
Coefficient of variation (CV)0.53972833
Kurtosis36.742687
Mean183.41821
Median Absolute Deviation (MAD)13.55
Skewness4.9778636
Sum53924.954
Variance9800.209
MonotonicityNot monotonic
2025-05-17T11:28:42.463096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188.4 2
 
0.7%
189.1 2
 
0.7%
189.2 2
 
0.7%
187.2 2
 
0.7%
187.3 2
 
0.7%
188.5 2
 
0.7%
188.6 2
 
0.7%
192.1 2
 
0.7%
192.2 2
 
0.7%
188.101 2
 
0.7%
Other values (250) 274
93.2%
ValueCountFrequency (%)
6.1 1
0.3%
7.1 1
0.3%
12.1 1
0.3%
12.2 1
0.3%
12.3 1
0.3%
12.4 1
0.3%
12.5 1
0.3%
16.1 1
0.3%
17.1 1
0.3%
41.1 1
0.3%
ValueCountFrequency (%)
910.1 1
0.3%
906.1 1
0.3%
902.1 1
0.3%
900.1 1
0.3%
234.1 1
0.3%
221.6 1
0.3%
221.5 1
0.3%
221.4 1
0.3%
221.3 1
0.3%
221.2 1
0.3%

treatment
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
CONTROL
247 
SUPP
47 

Length

Max length7
Median length7
Mean length6.5204082
Min length4

Characters and Unicode

Total characters1917
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONTROL
2nd rowSUPP
3rd rowCONTROL
4th rowCONTROL
5th rowCONTROL

Common Values

ValueCountFrequency (%)
CONTROL 247
84.0%
SUPP 47
 
16.0%

Length

2025-05-17T11:28:42.732599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:28:42.891687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
control 247
84.0%
supp 47
 
16.0%

Most occurring characters

ValueCountFrequency (%)
O 494
25.8%
C 247
12.9%
N 247
12.9%
T 247
12.9%
R 247
12.9%
L 247
12.9%
P 94
 
4.9%
S 47
 
2.5%
U 47
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 494
25.8%
C 247
12.9%
N 247
12.9%
T 247
12.9%
R 247
12.9%
L 247
12.9%
P 94
 
4.9%
S 47
 
2.5%
U 47
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 494
25.8%
C 247
12.9%
N 247
12.9%
T 247
12.9%
R 247
12.9%
L 247
12.9%
P 94
 
4.9%
S 47
 
2.5%
U 47
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 494
25.8%
C 247
12.9%
N 247
12.9%
T 247
12.9%
R 247
12.9%
L 247
12.9%
P 94
 
4.9%
S 47
 
2.5%
U 47
 
2.5%

length_mm
Real number (ℝ)

High correlation 

Distinct188
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.130952
Minimum42.9
Maximum100.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:43.101572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum42.9
5-th percentile58.665
Q166.1
median71.25
Q378.2
95-th percentile89.235
Maximum100.6
Range57.7
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation9.315371
Coefficient of variation (CV)0.12914527
Kurtosis0.41629263
Mean72.130952
Median Absolute Deviation (MAD)6
Skewness0.33673745
Sum21206.5
Variance86.776138
MonotonicityNot monotonic
2025-05-17T11:28:43.405056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.3 5
 
1.7%
78.2 4
 
1.4%
79.4 4
 
1.4%
75.1 4
 
1.4%
63.8 4
 
1.4%
69.9 4
 
1.4%
67.8 4
 
1.4%
69.1 3
 
1.0%
65.8 3
 
1.0%
74.1 3
 
1.0%
Other values (178) 256
87.1%
ValueCountFrequency (%)
42.9 1
0.3%
50 1
0.3%
51 1
0.3%
51.1 1
0.3%
52.3 1
0.3%
55.6 1
0.3%
55.7 1
0.3%
56 1
0.3%
56.4 1
0.3%
56.7 1
0.3%
ValueCountFrequency (%)
100.6 2
0.7%
97.7 1
0.3%
95.8 1
0.3%
95.1 1
0.3%
94.5 1
0.3%
93.6 1
0.3%
93.4 1
0.3%
93 1
0.3%
92.8 1
0.3%
91.5 1
0.3%

width_mm
Real number (ℝ)

High correlation 

Distinct185
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.116327
Minimum37.1
Maximum93.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:43.711769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum37.1
5-th percentile50.23
Q156.825
median61.2
Q367.3
95-th percentile76.645
Maximum93.9
Range56.8
Interquartile range (IQR)10.475

Descriptive statistics

Standard deviation8.015757
Coefficient of variation (CV)0.12904429
Kurtosis0.56501271
Mean62.116327
Median Absolute Deviation (MAD)5.05
Skewness0.3814099
Sum18262.2
Variance64.252361
MonotonicityNot monotonic
2025-05-17T11:28:44.021855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.5 5
 
1.7%
64 5
 
1.7%
57.8 5
 
1.7%
59.6 4
 
1.4%
57.4 4
 
1.4%
56.1 4
 
1.4%
56 4
 
1.4%
61.2 4
 
1.4%
60.1 4
 
1.4%
60 3
 
1.0%
Other values (175) 252
85.7%
ValueCountFrequency (%)
37.1 1
0.3%
43.1 1
0.3%
43.2 1
0.3%
44.9 1
0.3%
45.6 1
0.3%
48.2 1
0.3%
48.8 1
0.3%
48.9 1
0.3%
49 1
0.3%
49.1 1
0.3%
ValueCountFrequency (%)
93.9 1
0.3%
83.3 1
0.3%
81.5 1
0.3%
81.1 1
0.3%
80.6 1
0.3%
79.8 2
0.7%
78.6 1
0.3%
78.3 1
0.3%
77.8 1
0.3%
77.6 1
0.3%

ratio_len_wid
Real number (ℝ)

Distinct289
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1642564
Minimum0.87861272
Maximum1.4065744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:44.371419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.87861272
5-th percentile1.037338
Q11.1125592
median1.16692
Q31.2188372
95-th percentile1.3011647
Maximum1.4065744
Range0.52796168
Interquartile range (IQR)0.106278

Descriptive statistics

Standard deviation0.08445227
Coefficient of variation (CV)0.072537516
Kurtosis0.50173857
Mean1.1642564
Median Absolute Deviation (MAD)0.053912392
Skewness-0.022342232
Sum342.2914
Variance0.007132186
MonotonicityNot monotonic
2025-05-17T11:28:44.729461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.170825336 2
 
0.7%
1.211786372 2
 
0.7%
1.0984375 2
 
0.7%
1.302479339 2
 
0.7%
1.301164725 2
 
0.7%
1.124394184 1
 
0.3%
1.128252788 1
 
0.3%
1.196531792 1
 
0.3%
1.176352705 1
 
0.3%
1.142857143 1
 
0.3%
Other values (279) 279
94.9%
ValueCountFrequency (%)
0.8786127168 1
0.3%
0.909252669 1
0.3%
0.9322638146 1
0.3%
0.9539170507 1
0.3%
0.9739837398 1
0.3%
0.9843304843 1
0.3%
0.9904153355 1
0.3%
1.018612521 1
0.3%
1.019834711 1
0.3%
1.020527859 1
0.3%
ValueCountFrequency (%)
1.406574394 1
0.3%
1.401869159 1
0.3%
1.389261745 1
0.3%
1.38547486 1
0.3%
1.375518672 1
0.3%
1.362012987 1
0.3%
1.341897233 1
0.3%
1.337552743 1
0.3%
1.308584687 1
0.3%
1.303448276 1
0.3%

Flower_size
Real number (ℝ)

High correlation 

Distinct289
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.430961
Minimum15.9159
Maximum87.327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:45.091679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.9159
5-th percentile29.57954
Q137.74355
median44.3208
Q351.31095
95-th percentile67.392045
Maximum87.327
Range71.4111
Interquartile range (IQR)13.5674

Descriptive statistics

Standard deviation11.379063
Coefficient of variation (CV)0.25046935
Kurtosis0.85349571
Mean45.430961
Median Absolute Deviation (MAD)6.93195
Skewness0.76133981
Sum13356.702
Variance129.48308
MonotonicityNot monotonic
2025-05-17T11:28:45.411857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.781 2
 
0.7%
35.7294 2
 
0.7%
44.992 2
 
0.7%
47.674 2
 
0.7%
46.9982 2
 
0.7%
43.0824 1
 
0.3%
32.6566 1
 
0.3%
32.2299 1
 
0.3%
29.2913 1
 
0.3%
28.2296 1
 
0.3%
Other values (279) 279
94.9%
ValueCountFrequency (%)
15.9159 1
0.3%
22.45 1
0.3%
24.192 1
0.3%
24.3084 1
0.3%
24.888 1
0.3%
26.676 1
0.3%
27.9219 1
0.3%
28.2296 1
0.3%
28.7182 1
0.3%
28.861 1
0.3%
ValueCountFrequency (%)
87.327 1
0.3%
81.989 1
0.3%
81.0836 1
0.3%
79.8014 1
0.3%
77.9646 1
0.3%
75.9096 1
0.3%
70.8992 1
0.3%
70.05 1
0.3%
68.8503 1
0.3%
68.5875 1
0.3%

tunnel_length
Real number (ℝ)

High correlation 

Distinct114
Distinct (%)39.0%
Missing2
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean31.808219
Minimum22.4
Maximum42.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:45.764076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22.4
5-th percentile26.555
Q129.6
median31.3
Q334.025
95-th percentile37.045
Maximum42.6
Range20.2
Interquartile range (IQR)4.425

Descriptive statistics

Standard deviation3.3129032
Coefficient of variation (CV)0.10415243
Kurtosis-0.089361246
Mean31.808219
Median Absolute Deviation (MAD)2.3
Skewness0.085214943
Sum9288
Variance10.975327
MonotonicityNot monotonic
2025-05-17T11:28:46.131608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 11
 
3.7%
28.1 7
 
2.4%
30.7 7
 
2.4%
30.1 7
 
2.4%
31.5 6
 
2.0%
36.8 6
 
2.0%
29.5 6
 
2.0%
31.3 5
 
1.7%
34.3 5
 
1.7%
32.8 5
 
1.7%
Other values (104) 227
77.2%
ValueCountFrequency (%)
22.4 2
0.7%
23.9 1
0.3%
24.7 1
0.3%
25 1
0.3%
25.2 1
0.3%
25.3 2
0.7%
25.6 1
0.3%
25.7 1
0.3%
25.9 1
0.3%
26.4 2
0.7%
ValueCountFrequency (%)
42.6 1
 
0.3%
39.7 1
 
0.3%
39.6 1
 
0.3%
38.2 4
1.4%
38 2
0.7%
37.9 1
 
0.3%
37.6 2
0.7%
37.3 2
0.7%
37.1 1
 
0.3%
37 4
1.4%

entr_length
Real number (ℝ)

High correlation  Missing 

Distinct75
Distinct (%)25.9%
Missing4
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean13.135862
Minimum6.2
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:46.491733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile9.945
Q111.7
median13.1
Q314.6
95-th percentile16.62
Maximum18
Range11.8
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation2.0884317
Coefficient of variation (CV)0.15898703
Kurtosis0.19686158
Mean13.135862
Median Absolute Deviation (MAD)1.5
Skewness-0.14223706
Sum3809.4
Variance4.3615468
MonotonicityNot monotonic
2025-05-17T11:28:46.801877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.1 11
 
3.7%
14 9
 
3.1%
11.1 8
 
2.7%
12 8
 
2.7%
13.6 7
 
2.4%
15.7 7
 
2.4%
12.8 7
 
2.4%
12.4 7
 
2.4%
11.2 7
 
2.4%
14.1 7
 
2.4%
Other values (65) 212
72.1%
ValueCountFrequency (%)
6.2 2
0.7%
7.6 2
0.7%
8.3 2
0.7%
9.5 3
1.0%
9.8 2
0.7%
9.9 4
1.4%
10 2
0.7%
10.1 3
1.0%
10.2 1
 
0.3%
10.3 4
1.4%
ValueCountFrequency (%)
18 1
 
0.3%
17.7 1
 
0.3%
17.6 1
 
0.3%
17.5 4
1.4%
17.4 1
 
0.3%
17.3 1
 
0.3%
17.2 1
 
0.3%
17.1 1
 
0.3%
17 2
0.7%
16.9 1
 
0.3%

entr_height
Real number (ℝ)

High correlation  Missing 

Distinct62
Distinct (%)21.5%
Missing5
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean8.3515571
Minimum3.4
Maximum12.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:47.121464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3.4
5-th percentile5.8
Q17.3
median8.4
Q39.3
95-th percentile10.9
Maximum12.4
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.484786
Coefficient of variation (CV)0.17778553
Kurtosis0.44579519
Mean8.3515571
Median Absolute Deviation (MAD)0.9
Skewness-0.10358778
Sum2413.6
Variance2.2045896
MonotonicityNot monotonic
2025-05-17T11:28:47.481850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.9 17
 
5.8%
7.1 14
 
4.8%
7.8 11
 
3.7%
8.2 10
 
3.4%
7.9 10
 
3.4%
9.3 9
 
3.1%
9.4 9
 
3.1%
9.7 9
 
3.1%
8.1 9
 
3.1%
7.6 9
 
3.1%
Other values (52) 182
61.9%
ValueCountFrequency (%)
3.4 2
 
0.7%
5.1 3
1.0%
5.2 1
 
0.3%
5.5 1
 
0.3%
5.6 3
1.0%
5.7 2
 
0.7%
5.8 4
1.4%
5.9 2
 
0.7%
6.1 5
1.7%
6.2 3
1.0%
ValueCountFrequency (%)
12.4 3
1.0%
12 1
 
0.3%
11.4 1
 
0.3%
11.3 2
0.7%
11.2 2
0.7%
11.1 2
0.7%
11 2
0.7%
10.9 4
1.4%
10.8 1
 
0.3%
10.7 1
 
0.3%

tunnel_volume_cm
Real number (ℝ)

High correlation  Missing 

Distinct262
Distinct (%)90.7%
Missing5
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean3.559697
Minimum0.787338
Maximum7.1568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:47.791508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.787338
5-th percentile1.940622
Q12.708992
median3.392928
Q34.241568
95-th percentile5.6415084
Maximum7.1568
Range6.369462
Interquartile range (IQR)1.532576

Descriptive statistics

Standard deviation1.1245424
Coefficient of variation (CV)0.31590959
Kurtosis0.42411571
Mean3.559697
Median Absolute Deviation (MAD)0.729436
Skewness0.57275107
Sum1028.7524
Variance1.2645957
MonotonicityNot monotonic
2025-05-17T11:28:48.121592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.91096 2
 
0.7%
3.8794 2
 
0.7%
3.32465 2
 
0.7%
3.177872 2
 
0.7%
2.663492 2
 
0.7%
2.4544 2
 
0.7%
4.272048 2
 
0.7%
3.153465 2
 
0.7%
2.556036 2
 
0.7%
4.18562 2
 
0.7%
Other values (252) 269
91.5%
(Missing) 5
 
1.7%
ValueCountFrequency (%)
0.787338 2
0.7%
1.58137 1
0.3%
1.647712 1
0.3%
1.687504 1
0.3%
1.68948 1
0.3%
1.75788 1
0.3%
1.772004 1
0.3%
1.832787 1
0.3%
1.858975 2
0.7%
1.869056 1
0.3%
ValueCountFrequency (%)
7.1568 1
0.3%
6.998316 1
0.3%
6.926296 1
0.3%
6.7704 1
0.3%
6.67128 1
0.3%
6.318192 1
0.3%
6.297584 1
0.3%
6.069 1
0.3%
6.04044 1
0.3%
5.975892 1
0.3%

bp_area
Real number (ℝ)

High correlation 

Distinct286
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8062857
Minimum4.329
Maximum14.278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:48.441643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.329
5-th percentile5.49765
Q16.7805
median7.7125
Q38.66175
95-th percentile10.60135
Maximum14.278
Range9.949
Interquartile range (IQR)1.88125

Descriptive statistics

Standard deviation1.5583334
Coefficient of variation (CV)0.19962546
Kurtosis1.1226172
Mean7.8062857
Median Absolute Deviation (MAD)0.935
Skewness0.6693671
Sum2295.048
Variance2.428403
MonotonicityNot monotonic
2025-05-17T11:28:48.755220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.771 2
 
0.7%
7.652 2
 
0.7%
8.751 2
 
0.7%
8.418 2
 
0.7%
5.658 2
 
0.7%
7.778 2
 
0.7%
7.046 2
 
0.7%
5.452 2
 
0.7%
6.947 1
 
0.3%
9.572 1
 
0.3%
Other values (276) 276
93.9%
ValueCountFrequency (%)
4.329 1
0.3%
4.348 1
0.3%
4.593 1
0.3%
4.903 1
0.3%
5.038 1
0.3%
5.077 1
0.3%
5.139 1
0.3%
5.242 1
0.3%
5.281 1
0.3%
5.403 1
0.3%
ValueCountFrequency (%)
14.278 1
0.3%
13.563 1
0.3%
12.15 1
0.3%
11.838 1
0.3%
11.784 1
0.3%
11.481 1
0.3%
11.416 1
0.3%
11.262 1
0.3%
11.095 1
0.3%
11.024 1
0.3%

fruit
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
0.0
150 
1.0
144 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters882
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 150
51.0%
1.0 144
49.0%

Length

2025-05-17T11:28:49.021435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:28:49.181375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 150
51.0%
1.0 144
49.0%

Most occurring characters

ValueCountFrequency (%)
0 444
50.3%
. 294
33.3%
1 144
 
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 444
50.3%
. 294
33.3%
1 144
 
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 444
50.3%
. 294
33.3%
1 144
 
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 444
50.3%
. 294
33.3%
1 144
 
16.3%

seed
Real number (ℝ)

High correlation  Zeros 

Distinct45
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4421769
Minimum0
Maximum71
Zeros150
Zeros (%)51.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-05-17T11:28:49.401703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile40.7
Maximum71
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation14.461732
Coefficient of variation (CV)1.53161
Kurtosis2.6398428
Mean9.4421769
Median Absolute Deviation (MAD)0
Skewness1.7638088
Sum2776
Variance209.1417
MonotonicityNot monotonic
2025-05-17T11:28:49.712857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 150
51.0%
8 11
 
3.7%
2 11
 
3.7%
12 7
 
2.4%
7 7
 
2.4%
3 6
 
2.0%
9 6
 
2.0%
6 6
 
2.0%
34 5
 
1.7%
15 5
 
1.7%
Other values (35) 80
27.2%
ValueCountFrequency (%)
0 150
51.0%
1 1
 
0.3%
2 11
 
3.7%
3 6
 
2.0%
4 2
 
0.7%
5 4
 
1.4%
6 6
 
2.0%
7 7
 
2.4%
8 11
 
3.7%
9 6
 
2.0%
ValueCountFrequency (%)
71 1
 
0.3%
60 2
0.7%
58 2
0.7%
55 1
 
0.3%
52 1
 
0.3%
48 3
1.0%
46 1
 
0.3%
44 3
1.0%
42 1
 
0.3%
40 2
0.7%

Flower_size_norm_std
Real number (ℝ)

High correlation 

Distinct289
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0039147423
Minimum-2.5381548
Maximum3.5933885
Zeros0
Zeros (%)0.0%
Negative162
Negative (%)55.1%
Memory size2.4 KiB
2025-05-17T11:28:50.051820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.5381548
5-th percentile-1.364959
Q1-0.66397586
median-0.099236021
Q30.50095648
95-th percentile1.8817212
Maximum3.5933885
Range6.1315433
Interquartile range (IQR)1.1649323

Descriptive statistics

Standard deviation0.97703605
Coefficient of variation (CV)-249.57864
Kurtosis0.85349571
Mean-0.0039147423
Median Absolute Deviation (MAD)0.5951953
Skewness0.76133981
Sum-1.1509342
Variance0.95459945
MonotonicityNot monotonic
2025-05-17T11:28:50.405545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.175935948 2
 
0.7%
-0.8369160282 2
 
0.7%
-0.04160503925 2
 
0.7%
0.1886784743 2
 
0.7%
0.1306525241 2
 
0.7%
-0.2055682747 1
 
0.3%
-1.100754649 1
 
0.3%
-1.137392223 1
 
0.3%
-1.389708082 1
 
0.3%
-1.480868413 1
 
0.3%
Other values (279) 279
94.9%
ValueCountFrequency (%)
-2.538154803 1
0.3%
-1.977119939 1
0.3%
-1.827547277 1
0.3%
-1.817552869 1
0.3%
-1.767786902 1
0.3%
-1.61426456 1
0.3%
-1.507288337 1
0.3%
-1.480868413 1
0.3%
-1.438915942 1
0.3%
-1.426654762 1
0.3%
ValueCountFrequency (%)
3.593388453 1
0.3%
3.135053853 1
0.3%
3.057313848 1
0.3%
2.947220813 1
0.3%
2.789508375 1
0.3%
2.613060717 1
0.3%
2.182854708 1
0.3%
2.109940181 1
0.3%
2.006930811 1
0.3%
1.984366118 1
0.3%

tunnel_volume_cm_norm_std
Real number (ℝ)

High correlation  Missing 

Distinct262
Distinct (%)90.7%
Missing5
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean0.0055242385
Minimum-2.4809848
Maximum3.2317402
Zeros0
Zeros (%)0.0%
Negative156
Negative (%)53.1%
Memory size2.4 KiB
2025-05-17T11:28:50.681389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.4809848
5-th percentile-1.4466126
Q1-0.75746703
median-0.14404971
Q30.61708945
95-th percentile1.872686
Maximum3.2317402
Range5.712725
Interquartile range (IQR)1.3745565

Descriptive statistics

Standard deviation1.0085941
Coefficient of variation (CV)182.57613
Kurtosis0.42411571
Mean0.0055242385
Median Absolute Deviation (MAD)0.65422594
Skewness0.57275107
Sum1.5965049
Variance1.017262
MonotonicityNot monotonic
2025-05-17T11:28:51.001762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3205694941 2
 
0.7%
0.2922635554 2
 
0.7%
-0.2052877597 2
 
0.7%
-0.3369319001 2
 
0.7%
-0.7982756504 2
 
0.7%
-0.9858087726 2
 
0.7%
0.6444267438 2
 
0.7%
-0.3588223642 2
 
0.7%
-0.8946521697 2
 
0.7%
0.5669100875 2
 
0.7%
Other values (252) 269
91.5%
(Missing) 5
 
1.7%
ValueCountFrequency (%)
-2.480984781 2
0.7%
-1.768823148 1
0.3%
-1.709321482 1
0.3%
-1.673632321 1
0.3%
-1.67186006 1
0.3%
-1.610512589 1
0.3%
-1.597844874 1
0.3%
-1.543329035 1
0.3%
-1.519841205 2
0.7%
-1.510799628 1
0.3%
ValueCountFrequency (%)
3.231740221 1
0.3%
3.089597052 1
0.3%
3.025002828 1
0.3%
2.885180819 1
0.3%
2.796280798 1
0.3%
2.479598692 1
0.3%
2.461115523 1
0.3%
2.256100166 1
0.3%
2.230484905 1
0.3%
2.172592265 1
0.3%

bp_area_norm_std
Real number (ℝ)

High correlation 

Distinct286
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.00036238341
Minimum-2.1661136
Maximum4.030403
Zeros0
Zeros (%)0.0%
Negative159
Negative (%)54.1%
Memory size2.4 KiB
2025-05-17T11:28:51.303345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.1661136
5-th percentile-1.4382455
Q1-0.63925053
median-0.05877476
Q30.53244479
95-th percentile1.7404821
Maximum4.030403
Range6.1965166
Interquartile range (IQR)1.1716953

Descriptive statistics

Standard deviation0.9705738
Coefficient of variation (CV)-2678.3063
Kurtosis1.1226172
Mean-0.00036238341
Median Absolute Deviation (MAD)0.58234425
Skewness0.6693671
Sum-0.10654072
Variance0.94201349
MonotonicityNot monotonic
2025-05-17T11:28:51.630851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6004887613 2
 
0.7%
-0.09645585836 2
 
0.7%
0.5880321997 2
 
0.7%
0.3806304497 2
 
0.7%
-1.338375047 2
 
0.7%
-0.01797952049 2
 
0.7%
-0.4738896738 2
 
0.7%
-1.466677631 2
 
0.7%
-0.5355496536 1
 
0.3%
1.099374052 1
 
0.3%
Other values (276) 276
93.9%
ValueCountFrequency (%)
-2.166113563 1
0.3%
-2.154279829 1
0.3%
-2.00168695 1
0.3%
-1.808610246 1
0.3%
-1.724528455 1
0.3%
-1.70023816 1
0.3%
-1.661622819 1
0.3%
-1.597471527 1
0.3%
-1.573181232 1
0.3%
-1.497196207 1
0.3%
ValueCountFrequency (%)
4.030402989 1
0.3%
3.585080913 1
0.3%
2.705024838 1
0.3%
2.510702478 1
0.3%
2.477069761 1
0.3%
2.288352854 1
0.3%
2.247869029 1
0.3%
2.151953504 1
0.3%
2.047941215 1
0.3%
2.003720422 1
0.3%

seed_norm_std
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3594568 × 10-17
Minimum-0.65402097
Maximum4.2638586
Zeros0
Zeros (%)0.0%
Negative204
Negative (%)69.4%
Memory size2.4 KiB
2025-05-17T11:28:51.942288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.65402097
5-th percentile-0.65402097
Q1-0.65402097
median-0.65402097
Q30.31570177
95-th percentile2.1651016
Maximum4.2638586
Range4.9178796
Interquartile range (IQR)0.96972273

Descriptive statistics

Standard deviation1.001705
Coefficient of variation (CV)7.3684214 × 1016
Kurtosis2.6398428
Mean1.3594568 × 10-17
Median Absolute Deviation (MAD)0
Skewness1.7638088
Sum-6.6613381 × 10-15
Variance1.003413
MonotonicityNot monotonic
2025-05-17T11:28:52.273408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
-0.6540209699 150
51.0%
-0.09989369281 11
 
3.7%
-0.5154891506 11
 
3.7%
0.1771699457 7
 
2.4%
-0.1691596024 7
 
2.4%
-0.446223241 6
 
2.0%
-0.03062778317 6
 
2.0%
-0.2384255121 6
 
2.0%
1.701019958 5
 
1.7%
0.3849676746 5
 
1.7%
Other values (35) 80
27.2%
ValueCountFrequency (%)
-0.6540209699 150
51.0%
-0.5847550603 1
 
0.3%
-0.5154891506 11
 
3.7%
-0.446223241 6
 
2.0%
-0.3769573314 2
 
0.7%
-0.3076914217 4
 
1.4%
-0.2384255121 6
 
2.0%
-0.1691596024 7
 
2.4%
-0.09989369281 11
 
3.7%
-0.03062778317 6
 
2.0%
ValueCountFrequency (%)
4.263858614 1
 
0.3%
3.501933608 2
0.7%
3.363401789 2
0.7%
3.15560406 1
 
0.3%
2.947806331 1
 
0.3%
2.670742693 3
1.0%
2.532210873 1
 
0.3%
2.393679054 3
1.0%
2.255147235 1
 
0.3%
2.116615416 2
0.7%

fruit_norm_std
Categorical

High correlation 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
-0.9966610925150702
150 
1.0033500931359767
144 

Length

Max length19
Median length19
Mean length18.510204
Min length18

Characters and Unicode

Total characters5442
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0033500931359767
2nd row1.0033500931359767
3rd row1.0033500931359767
4th row-0.9966610925150702
5th row-0.9966610925150702

Common Values

ValueCountFrequency (%)
-0.9966610925150702 150
51.0%
1.0033500931359767 144
49.0%

Length

2025-05-17T11:28:52.594468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:28:52.761831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.9966610925150702 150
51.0%
1.0033500931359767 144
49.0%

Most occurring characters

ValueCountFrequency (%)
0 1176
21.6%
9 738
13.6%
6 594
10.9%
1 588
10.8%
5 588
10.8%
3 576
10.6%
7 438
 
8.0%
2 300
 
5.5%
. 294
 
5.4%
- 150
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1176
21.6%
9 738
13.6%
6 594
10.9%
1 588
10.8%
5 588
10.8%
3 576
10.6%
7 438
 
8.0%
2 300
 
5.5%
. 294
 
5.4%
- 150
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1176
21.6%
9 738
13.6%
6 594
10.9%
1 588
10.8%
5 588
10.8%
3 576
10.6%
7 438
 
8.0%
2 300
 
5.5%
. 294
 
5.4%
- 150
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1176
21.6%
9 738
13.6%
6 594
10.9%
1 588
10.8%
5 588
10.8%
3 576
10.6%
7 438
 
8.0%
2 300
 
5.5%
. 294
 
5.4%
- 150
 
2.8%

Interactions

2025-05-17T11:28:31.049425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:23.407120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:27.337607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:30.526624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:33.921685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:36.775625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:40.245412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:44.081561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:48.999131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:53.269720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:57.671597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:02.797431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:06.663252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:10.861455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:14.841734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:19.961722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:23.684424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:27.281631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:31.291803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:23.586037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:27.483198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:30.701428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:34.074076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:36.985922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:40.375654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:44.292801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:49.233694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:53.534728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:57.911834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:03.001435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:06.891448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:11.085541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:15.031634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:20.173193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:23.881639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:27.491554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:31.516939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:23.921401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:27.601715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:30.874597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:34.231483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:37.167567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:40.591463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:44.517809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:49.485046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:53.805455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:58.141414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:03.221442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:07.131661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:11.291775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:15.263756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:20.367442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:24.093282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:27.719714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:31.741678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:24.237574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:27.751463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:31.421668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:34.351674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:37.361445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:40.831645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:44.755039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-17T11:28:05.825836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:09.915207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:14.035487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:17.938192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:22.866075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:26.541420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:30.241879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:34.211643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:26.914789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:29.970877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:33.323524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:36.267593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:39.851360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:43.391365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:47.531401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:52.582391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:56.904506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:01.151811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:06.034762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:10.162956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:14.241643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:18.151460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:23.063750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:26.723865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:30.431749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:34.381735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:27.052231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:30.141596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:33.515647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:36.431651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:39.975826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:43.636063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:48.543911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:52.821583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:57.161783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:02.321789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:06.252775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:10.379624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:14.458456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:18.361554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:23.294158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:26.934188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:30.611714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:34.581861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:27.191543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:30.305585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:33.721727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:36.601574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:40.106171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:43.871403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:48.795825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:53.051672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:27:57.421719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:02.567447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:06.463780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-17T11:28:14.647175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:19.777367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:23.491654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:27.122568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:28:30.823251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-17T11:28:52.997354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Flower_idFlower_sizeFlower_size_norm_stdS.No.areabp_areabp_area_norm_stddatedaydf_indexentr_heightentr_lengthfruitfruit_norm_stdlength_mmpopulationratio_len_widseedseed_norm_stdtreatmenttunnel_lengthtunnel_volume_cmtunnel_volume_cm_norm_stdwidth_mm
Flower_id1.0000.0460.0460.2780.389-0.052-0.0520.2590.0470.2780.137-0.0280.0000.0000.0760.4340.061-0.042-0.0420.162-0.0680.0150.0150.017
Flower_size0.0461.0001.000-0.5610.237-0.018-0.0180.248-0.103-0.5610.1520.2360.0240.0240.9570.551-0.0150.1080.1080.2360.4760.3580.3580.950
Flower_size_norm_std0.0461.0001.000-0.5610.237-0.018-0.0180.248-0.103-0.5610.1520.2360.0240.0240.9570.551-0.0150.1080.1080.2360.4760.3580.3580.950
S.No.0.278-0.561-0.5611.0000.3690.0100.0100.7290.3971.000-0.066-0.1730.0000.000-0.5560.947-0.063-0.105-0.1050.479-0.288-0.216-0.216-0.517
area0.3890.2370.2370.3691.0000.0940.0940.2870.2360.3690.0910.0580.0000.0000.2150.7080.0000.0000.0000.3730.1620.1300.1300.256
bp_area-0.052-0.018-0.0180.0100.0941.0001.0000.0000.0840.010-0.099-0.0340.0000.000-0.0400.071-0.071-0.028-0.0280.109-0.015-0.079-0.0790.019
bp_area_norm_std-0.052-0.018-0.0180.0100.0941.0001.0000.0000.0840.010-0.099-0.0340.0000.000-0.0400.071-0.071-0.028-0.0280.109-0.015-0.079-0.0790.019
date0.2590.2480.2480.7290.2870.0000.0001.0000.9950.7290.0000.0790.0000.0000.2880.9810.1250.0000.0000.5280.1240.0000.0000.278
day0.047-0.103-0.1030.3970.2360.0840.0840.9951.0000.397-0.064-0.1650.0000.000-0.1120.777-0.089-0.026-0.0260.4900.011-0.114-0.114-0.086
df_index0.278-0.561-0.5611.0000.3690.0100.0100.7290.3971.000-0.066-0.1730.0000.000-0.5560.947-0.063-0.105-0.1050.479-0.288-0.216-0.216-0.517
entr_height0.1370.1520.152-0.0660.091-0.099-0.0990.000-0.064-0.0661.0000.2070.0000.0000.1750.0000.105-0.000-0.0000.0000.1600.6860.6860.111
entr_length-0.0280.2360.236-0.1730.058-0.034-0.0340.079-0.165-0.1730.2071.0000.0000.0000.2010.0000.009-0.002-0.0020.0540.4250.7560.7560.251
fruit0.0000.0240.0240.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.9930.1040.0640.0000.7530.7530.1050.0000.0000.0000.185
fruit_norm_std0.0000.0240.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.9931.0000.1040.0640.0000.7530.7530.1050.0000.0000.0000.185
length_mm0.0760.9570.957-0.5560.215-0.040-0.0400.288-0.112-0.5560.1750.2010.1040.1041.0000.5300.2410.1090.1090.2580.4600.3450.3450.826
population0.4340.5510.5510.9470.7080.0710.0710.9810.7770.9470.0000.0000.0640.0640.5301.0000.0000.0000.0000.4450.2740.0000.0000.498
ratio_len_wid0.061-0.015-0.015-0.0630.000-0.071-0.0710.125-0.089-0.0630.1050.0090.0000.0000.2410.0001.000-0.033-0.0330.1610.0080.0490.049-0.289
seed-0.0420.1080.108-0.1050.000-0.028-0.0280.000-0.026-0.105-0.000-0.0020.7530.7530.1090.000-0.0331.0001.0000.1440.0610.0200.0200.108
seed_norm_std-0.0420.1080.108-0.1050.000-0.028-0.0280.000-0.026-0.105-0.000-0.0020.7530.7530.1090.000-0.0331.0001.0000.1440.0610.0200.0200.108
treatment0.1620.2360.2360.4790.3730.1090.1090.5280.4900.4790.0000.0540.1050.1050.2580.4450.1610.1440.1441.0000.1480.0000.0000.331
tunnel_length-0.0680.4760.476-0.2880.162-0.015-0.0150.1240.011-0.2880.1600.4250.0000.0000.4600.2740.0080.0610.0610.1481.0000.6330.6330.455
tunnel_volume_cm0.0150.3580.358-0.2160.130-0.079-0.0790.000-0.114-0.2160.6860.7560.0000.0000.3450.0000.0490.0200.0200.0000.6331.0001.0000.342
tunnel_volume_cm_norm_std0.0150.3580.358-0.2160.130-0.079-0.0790.000-0.114-0.2160.6860.7560.0000.0000.3450.0000.0490.0200.0200.0000.6331.0001.0000.342
width_mm0.0170.9500.950-0.5170.2560.0190.0190.278-0.086-0.5170.1110.2510.1850.1850.8260.498-0.2890.1080.1080.3310.4550.3420.3421.000

Missing values

2025-05-17T11:28:34.971803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-17T11:28:35.551404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-17T11:28:36.041589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexS.No.datedaypopulationareagenotype_idFlower_idtreatmentlength_mmwidth_mmratio_len_widFlower_sizetunnel_lengthentr_lengthentr_heighttunnel_volume_cmbp_areafruitseedFlower_size_norm_stdtunnel_volume_cm_norm_stdbp_area_norm_stdseed_norm_stdfruit_norm_std
0012024-02-071NETBLUEN119119.1CONTROL100.681.51.23435681.989034.114.18.94.2792098.1661.08.03.1350540.6508490.223678-0.0998941.003350
1122024-02-071NETBROWNN185185.1SUPP84.077.21.08808364.848033.611.67.83.0401287.2241.055.01.663283-0.460474-0.3630263.1556041.003350
2232024-02-071NETREDN186186.1CONTROL94.572.51.30344868.512531.016.28.94.4695807.6521.039.01.9779260.821592-0.0964562.0473501.003350
3342024-02-071NETREDN186186.2CONTROL91.570.51.29787264.507528.011.87.92.6101607.9580.00.01.634047-0.8461090.094130-0.654021-0.996661
4452024-02-071NETGREENN187187.1CONTROL95.171.11.33755367.616131.416.87.74.0619045.6560.00.01.9009590.455950-1.339621-0.654021-0.996661
5562024-02-071NETGREENN188188.1CONTROL92.876.41.21466070.899231.514.48.73.9463206.9470.00.02.1828550.352284-0.535550-0.654021-0.996661
6672024-02-071NETGREENN188188.2CONTROL74.171.41.03781552.907431.514.65.82.6674209.5721.021.00.638032-0.7947531.0993740.8005631.003350
7782024-02-071NETGREENN188188.3CONTROL78.774.81.05213958.867630.713.59.33.8543857.5011.030.01.1497900.269828-0.1905031.4239561.003350
8892024-02-071NETGREENN188188.4CONTROL78.260.11.30116546.998228.111.010.43.2146407.5880.00.00.130653-0.303955-0.136317-0.654021-0.996661
99102024-02-115NETBLUEN0077.1CONTROL65.958.51.12649638.551531.514.79.24.2600607.7781.015.0-0.5946030.633675-0.0179800.3849681.003350
df_indexS.No.datedaypopulationareagenotype_idFlower_idtreatmentlength_mmwidth_mmratio_len_widFlower_sizetunnel_lengthentr_lengthentr_heighttunnel_volume_cmbp_areafruitseedFlower_size_norm_stdtunnel_volume_cm_norm_stdbp_area_norm_stdseed_norm_stdfruit_norm_std
2842892902024-03-0730KURBLUEK220220.2CONTROL60.350.81.18700830.632427.313.69.13.3786488.0081.018.0-1.274558-0.1568570.1252710.5927651.003350
2852902912024-03-0730KURGREENK173173.3CONTROL66.051.01.29411833.660032.811.39.13.3728245.2810.00.0-1.014600-0.162081-1.573181-0.654021-0.996661
2862912922024-03-0730KURREDK221221.1CONTROL68.664.01.07187543.904034.714.08.44.0807209.1871.029.0-0.1350240.4728260.8595851.3546901.003350
2872922932024-03-0730KURREDK221221.2CONTROL67.856.01.21071437.968034.610.47.42.6628168.7841.018.0-0.644704-0.7988820.6085860.5927651.003350
2882932942024-03-0730KURREDK221221.3CONTROL67.761.31.10440541.500132.712.68.83.6257768.3640.00.0-0.3414290.0647900.346998-0.654021-0.996661
2892942952024-03-0730KURREDK221221.4CONTROL66.854.71.22120736.539631.811.97.82.9516767.8850.00.0-0.767350-0.5398050.048663-0.654021-0.996661
2902982992024-03-1336KURBLUEK204204.2CONTROL70.259.31.18381141.628632.311.08.22.9134605.4520.00.0-0.330395-0.574081-1.466678-0.654021-0.996661
2912993002024-03-1336KURBrownK159159.5CONTROL42.937.11.15633415.915922.410.99.72.3683528.9191.013.0-2.538155-1.0629850.6926670.2464361.003350
2923003012024-03-1336KURREDK221221.5CONTROL60.952.31.16443631.850730.713.55.82.4038107.4560.00.0-1.169951-1.031183-0.218530-0.654021-0.996661
2933013022024-03-1336KURREDK221221.6CONTROL59.554.31.09576432.308531.012.65.82.2654808.5410.00.0-1.130643-1.1552500.457238-0.654021-0.996661